issue_comments: 601885539

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https://github.com/pydata/xarray/pull/2922#issuecomment-601885539 https://api.github.com/repos/pydata/xarray/issues/2922 601885539 MDEyOklzc3VlQ29tbWVudDYwMTg4NTUzOQ== 7441788 2020-03-20T19:57:54Z 2020-03-20T20:00:20Z CONTRIBUTOR

All good points:

What could be done, though is to only do da = da.fillna(0.0) if da contains NaNs.

Good idea, though I don't know what the performance hit would be of the extra check (in the case that da does contain NaNs, so the check is for naught).

I assume so. I don't know what kind of temporary variables np.einsum creates. Also np.einsum is wrapped in xr.apply_ufunc so all kinds of magic is going on.

Well, (da * weights) will be at least as large as da. I'm not certain, but I don't think np.einsum creates huge temporary arrays.

Do you want to leave it away for performance reasons? Because it was a deliberate decision to not support NaNs in the weights and I don't think this is going to change.

Yes. You can continue not supporting NaNs in the weights, yet not explicitly check that there are no NaNs (optionally, if the caller assures you that there are no NaNs).

None of your suggested functions support NaNs so they won't work.

Correct. These have nothing to do with the NaNs issue.

For profiling memory usage, I use psutil.Process(os.getpid()).memory_info().rss for current usage and resource.getusage(resource.RUSAGE_SElF).ru_maxrss for peak usage (on linux).

 
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